The present invention relates to medical imaging of the brain, and more particularly, to fully automatic segmentation of brain tumors in multi-spectral 3D magnetic resonance images.
Detection and delineation of pathology, such as cancerous tissue, in multi-spectral brain magnetic resonance (MR) volume sequences is an important problem in medical imaging analysis. For example, precise and reliable segmentation of brain tumors is critical in order to automatically extract diagnostically relevant quantitative findings regarding the brain tumors, such as the volume of a tumor or its relative location. Once these findings are obtained, they can be used for guiding computer-aided diagnosis and therapy planning, as well as for traditional decision making, such as decisions regarding surgery. However, the manual labeling of MR volumetric data is time consuming, which can lead to potential delays in the clinical workflow. Furthermore, manual annotations may vary significantly among experts as a result of individual experience and interpretation. Accordingly, a method for fully automatically segmenting brain tumors in MR volumes without user interaction is desirable.
Further, in 3D MR images of pediatric brains, significant variation of shape and appearance can be caused not only by pathology (tumors), but also by non-pathological “background”, which is caused by ongoing myelination of white matter during maturation of the brain. This results in heterogeneous shapes and appearance of pediatric brain tumors in different patients. Thus, the segmentation of pediatric brain tumors is more complicated than the segmentation of brain tumors in adult patients. Accordingly, a brain tumor segmentation that is robust enough to accurately handle the segmentation of pediatric brain tumors is desirable.